These four aspects also affect the effective model complexity. In general, model framework and model size decide the available range of effective model complexity.

The effective complexity of a model with fixed parameters is a value in this range selected by optimization process and training data. The optimization process affects the effective complexity, for example, adding \(L^1\)  regularization constrains the degrees of freedom in a deep neural network, and thus constrains the effective model complexity [1]}, [2]}.
The training data affects the effecive complexity, for example, using the same model and the same optimization process, the effective complexity of a model trained on linearly classifiable data is much lower than that trained on the ImageNet [3]}, [4]}.
Specifically, the effect of training data complexity and optimization process on effective complexity are reflected in the values of model parameters.
